Showing items with similar title or keywords.

• Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo 

  Vihola, Matti; Helske, Jouni; Franks, Jordan (Wiley-Blackwell, 2020)

  We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the ...

• Conditional particle filters with diffuse initial distributions 

  Karppinen, Santeri; Vihola, Matti (Springer, 2021)

  Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which ...

• Statistical analysis of life sequence data 

  Helske, Satu (University of Jyväskylä, 2016)

• Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R 

  Helske, Jouni (Elsevier BV, 2022)

  The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as ...

• On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction 

  Vihola, Matti; Franks, Jordan (Oxford University Press, 2020)

  Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation ...